Input and Output.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = pd.read_csv(\"https://data.cityofnewyork.us/api/views/ic3t-wcy2/rows.csv\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.read_csv(\"https://data.cityofnewyork.us/api/views/ic3t-wcy2/rows.csv\").head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t.to_csv(\"test_Export.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t.to_csv(\"test_Export.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime_india = pd.read_csv(\"https://data.gov.in/node/4223881/datastore/export/csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crime_india.to_csv(\"crime_india.csv\",index=False,encoding=\"UTF8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>City</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>James</td>\n",
       "      <td>Miami</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sean</td>\n",
       "      <td>Hawkins</td>\n",
       "      <td>Denver</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Judy</td>\n",
       "      <td>Day</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ashley</td>\n",
       "      <td>Ruiz</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stephanie</td>\n",
       "      <td>Gomez</td>\n",
       "      <td>Portland</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  First Name Last Name           City Gender\n",
       "0    Brandon     James          Miami      M\n",
       "1       Sean   Hawkins         Denver      M\n",
       "2       Judy       Day    Los Angeles      F\n",
       "3     Ashley      Ruiz  San Francisco      F\n",
       "4  Stephanie     Gomez       Portland      F"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "pd.read_excel(\"Data - Multiple Worksheets.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>City</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Parker</td>\n",
       "      <td>Power</td>\n",
       "      <td>Raleigh</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Preston</td>\n",
       "      <td>Prescott</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ronaldo</td>\n",
       "      <td>Donaldo</td>\n",
       "      <td>Bangor</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Megan</td>\n",
       "      <td>Stiller</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bustin</td>\n",
       "      <td>Jieber</td>\n",
       "      <td>Austin</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  First Name Last Name           City Gender\n",
       "0     Parker     Power        Raleigh      F\n",
       "1    Preston  Prescott   Philadelphia      F\n",
       "2    Ronaldo   Donaldo         Bangor      M\n",
       "3      Megan   Stiller  San Francisco      M\n",
       "4     Bustin    Jieber         Austin      F"
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "pd.read_excel(\"Data - Multiple Worksheets.xlsx\", sheetname=1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([(0,   First Name Last Name           City Gender\n",
       "              0    Brandon     James          Miami      M\n",
       "              1       Sean   Hawkins         Denver      M\n",
       "              2       Judy       Day    Los Angeles      F\n",
       "              3     Ashley      Ruiz  San Francisco      F\n",
       "              4  Stephanie     Gomez       Portland      F),\n",
       "             (1,   First Name Last Name           City Gender\n",
       "              0     Parker     Power        Raleigh      F\n",
       "              1    Preston  Prescott   Philadelphia      F\n",
       "              2    Ronaldo   Donaldo         Bangor      M\n",
       "              3      Megan   Stiller  San Francisco      M\n",
       "              4     Bustin    Jieber         Austin      F)])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel(\"Data - Multiple Worksheets.xlsx\", sheetname=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "state = crime_india[\"State/UT (Col.3)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "excel_file = pd.ExcelWriter(\"State Name.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "state.to_excel(excel_file,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "excel_file.save()"
   ]
  },
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